0% Complete
Home
/
14th International Conference on Computer and Knowledge Engineering
Deep Learning-Based Malaysian Sign Language (MSL) Recognition: Exploring the Impact of Color Spaces
Authors :
Ervin Gubin Moung
1
Precilla Fiona Suwek
2
Maisarah Mohd Sufian
3
Valentino Liaw
4
Ali Farzamnia
5
Wei Leong Khong
6
1- Faculty of Computing and Informatics University Malaysia Sabah
2- Faculty of Computing and Informatics University Malaysia Sabah
3- Faculty of Computing and Informatics Universiti Malaysia Sabah
4- Faculty of Computing and Informatics Universiti Malaysia Sabah
5- School of Computing and Engineering University of Huddersfield
6- School of Engineering Monash University Malaysia
Keywords :
sign language،Malaysian Sign Language،color space،ResNet18،Convolutional Neural Network (CNN)
Abstract :
Sign Language is one form of communication for this group of people to communicate with each other. Not only for people with hearing problems but sign language is also useful for people who are mute or have problem speaking. The most used sign language is the American Sign Language (ASL) that is widely used in English speaking countries. In Malaysia, Bahasa Isyarat Malaysia (BIM) or Malaysian Sign Language (MSL) is still new to the community in Malaysia. In this project, a dataset with 5980 images of the signed alphabet is used to train models to recognize what the signs mean. The problem this project aims to address is the limited research and available datasets in the field of Malaysian Sign Language (MSL) recognition using deep learning and various color spaces. Two models that are used are Convolutional Neural Network (CNN) and Residual Network 18 (ResNet18). The images are also converted into different color spaces which are RGB, YCbCr, Grayscale and the combination of RGB and YCbCr. From the results, RGB is the best color space with CNN without any image processing technique - 80% testing accuracy, with Histogram Equalization (HE) - 82.40% testing accuracy, and with Contrast Limited Adaptive HE (CLAHE) - 83.90%. Whereas YCbCr is the best color space when using ResNet18 without any image processing technique - 88% testing accuracy, with HE - 84.40% testing accuracy, and with CLAHE - 88.30%. The precision, recall, and F1-score metrics are also have been used to evaluate the efficacy of the suggested system.
Papers List
List of archived papers
Automatic Generation of XACML Code using Model-Driven Approach
Athareh Fatemian - Bahman Zamani - Marzieh Masoumi - Mehran Kamranpour - Behrouz Tork Ladani - Shekoufeh Kolahdouz Rahimi
EEMC: Energy Efficient Multi-Clustering Using Grey Wolf Optimizer in WSNs
Maryam Ghorbanvirdi - Sayyed Majid Mazinani
Android Malware Detection using Supervised Deep Graph Representation Learning
Fatemeh Deldar - Mahdi Abadi - Mohammad Ebrahimifard
Semantic Segmentation Using Region Proposals and Weakly-Supervised Learning
Maryam Taghizadeh - Abdolah Chalechale
Real-Time Vehicle Detection and Classification in UAV imagery Using Improved YOLOv5
Mohammad Hossein Hamzenejadi - Hadis Mohseni
Deep Learning Based High-Resolution Edge Detection for Microwave Imaging using a Variational Autoencoder
Seyed Reza Razavi Pour - Leila Ahmadi - Amir Ahmad Shishegar
Supervised Contrastive Learning for Short Text Classification in Natural Language Processing
Mitra Esmaeili - Hamed Vahdat nejad
ExaAEC: A New Multi-label Emotion Classification Corpus in Arabic Tweets
Saeed Sarbazi-Azad - Ahmad Akbari - Mohsen Khazeni
Distilled BERT Model In Natural Language Processing
Yazdan Zandiye Vakili - Avisa Fallah - Hedieh Sajedi
Efficient Sub-Carrier Relationship Extraction for Human Activity Recognition via EEGNet in Wireless Sensing
Siavash Zaravashan - Sadegh ArefiZadeh - Sajjad Torabi
more
Samin Hamayesh - Version 42.2.1